- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Research on Image Smoothing Diffusion Model with Gradient and Curvature Features
摘要： In this paper, two image smoothing models are proposed for the visual inspection of high-density flexible IC package substrates with strict requirements on line-width and line distance which are applied to the de-noising of high-density flexible IC package substrate images. First of all, the two models proposed in this paper combines the level set curvature feature of the image with gradient threshold, using more abundant second-order differential information as the detection factor to remove noise in image. Secondly, theoretical analysis shows that the de-noised image obtained by the two models proposed can retain more detailed texture information and edge information of the original image. What is more, the experimental analysis shows that the proposed models have the highest structural similarity and peak signal-to-noise ratio, and have a relatively high edge-preserving index and the lowest mean squared error compared with other models. In particular, the de-noised image through Model 1 has the highest structural similarity and peak signal to noise ratio, as well as the lowest mean squared error. The de-noised image through Model 2 has a relatively high edge retention index. The methods proposed in this paper can effectively remove the noise of the image of the high-density flexible IC package substrate, and can retain the original details and edges information of the image.
关键词： Image de-noising,Flexible integrated circuit substrate image,Gradient,Curvature
更新于2025-09-23 15:22:29
-
A Novel Two Dimensional Adaptive Filtering Algorithm for Image De-Noising via Fractional Gradient
摘要： In the recent decade it has been witnessed that raster images are the primary source of law enforcement, information for numerous applications such as bio-medical, geographical information system (GIS), photography, astronomy, etc. Primarily, the quality of raster images compromises due to the surrounding factors of these applications. Because, it is very difficult to control surrounding parameter (light, motion, distance) while acquiring images. Therefore, the image acquisition in these applications is very much prone to the noise. In the literature, researchers have targeted this issue and have already devised classical image filters for image de-noising. Afterwards, in the recent years the performance of classical filtering was further improved by employing two dimensional adaptive filters (2-DAF) for image de-noising and enhancement. In the literature, researchers have reported the performance comparisons of various 2-DAF specifically for image restoration, enhancement, estimation, and de-noising. In this paper an extended version of one dimensional fractional least mean square (1-DFLMS) to two dimensional fractional least mean square (2-DFLMS) is presented. Moreover the performance of the proposed algorithm has been rigorously compared with the existing and most employed 2-DAF algorithm namely, two dimensional least mean square (2-DLMS), two dimensional variable step size least mean square (2-DVSSLMS). The simulation results illustrate the notable performance edge of the proposed algorithm with the existing approaches.
关键词： Image de-noising,least mean square (LMS),fractional least mean square (FLMS),two-dimensional adaptive filtering,variable step size least mean square (VSSLMS)
更新于2025-09-23 15:21:01
-
[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Optimization BM3D Algorithm Based on Pseudo-3D Transform
摘要： A optimization BM3D algorithm based on pseudo-3D transformation is proposed to solve the problem of huge computational of BM3D algorithm. Firstly, one-dimensional wavelet transform was carried out on the third dimension of 3D similar blocks to get one-dimensional the lowest-frequency coefficient block gotten above was transformed by two-dimensional transformation. Following, two-dimensional inverse transformation was performed on the lowest-frequency coefficient blocks after hard threshold filtering or Wiener filtering. And finally, one-dimensional inverse transformation was carried out on the third dimension to get the noise reduced image. The performance of the proposed optimization method is very closed to the original BM3D algorithm, but computation of pseudo-3D transformation is only 1/3 of that of 3D transformation.
关键词： BM3D,Pseudo-3D Transform,Similar Image Blocks,Image De-noising
更新于2025-09-23 15:19:57
-
[IEEE 2018 International Conference on Intelligent and Advanced System (ICIAS) - Kuala Lumpur, Malaysia (2018.8.13-2018.8.14)] 2018 International Conference on Intelligent and Advanced System (ICIAS) - Study of Various Image De-Noising Methods Used for the Purpose of Traffic Sign Board Recognition in an Intelligent Advanced Driver Assistance System
摘要： As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating; slowing down the speed of vehicle since chances of having collision cannot be ruled out. With constant increasing of the training database size, not only the recognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as removing the noise present in a particular image with the help of Arithmetic Mean Filter as well as Geometric Mean Filter. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board.
关键词： Color,Geometric Mean Filter,Image De-noising,Arithmetic Mean Filter,Shape
更新于2025-09-04 15:30:14
-
[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Phase Matching Guided Filtering
摘要： Guided ?ltering (GF) is widely employed to generate a de-noised image from a ?ash image and a no-?ash image. To generate a de-noised image with the GF, a ?ash image and a no-?ash image are utilized as an input image and a guidance image of the GF, respectively. However, the captured ?ash and no-?ash images often suffer from phase-mismatching caused by hand-shaking, and therefore the resultant image may look blurry. To solve this problem, this paper presents an adaptive phase matching guided ?ltering. By applying the Taylor series approximation to the conventional GF, the proposed method can generate the phase corrected result image. Experimental results are presented to validate the superiority of the proposed algorithm.
关键词： guided ?ltering,?ash/no-?ash image,de-noising
更新于2025-09-04 15:30:14
-
Two stage image de-noising by SVD on large scale heterogeneous anisotropic diffused image data
摘要： De-noising of images along with the edge enhancement has always been a challenging task in large scale heterogeneous image data. This paper presents a two stage image de-noising as well as edge enhancement method where in the first stage two copies of input noisy image are created through diffusion. The first copy is got by using anisotropic diffusion method which employ optimal diffusion function while the second copy is generated to improve the sharp edges by applying the combination of inverse heat diffusion and Canny edge detector. In the next stage, the singular value decomposition is applied on the two copies achieved in first stage to reduce the noise and improve the quality of detected edges. The optimal number of significant singular values have been estimated by the analysis of signal to noise ratio of singular value decomposed images of first copy. The singular values extracted from the second copy of the diffused image are superimposed with non decreasing weights from linear weighting function. Finally the sharp edged and noise reduced output image is generated by taking the linear combination of two singular value decomposed images. The performance of the proposed method has been compared with existing methods based on singular value decomposition as well as anisotropic diffusion. The experimental results exhibit that the proposed method efficiently enhances the edges by reducing the noisy significantly.
关键词： Image de-noising,Anisotropic diffusion,Edge enhancement,Singular value decomposition
更新于2025-09-04 15:30:14